Machine Learning Engineer

Role Overview

Machine Learning Engineer for the Security team at CloudWalk, building an AI intelligence layer for edge defenses. The role focuses on designing, training, and deploying ML models that detect anomalies, mitigate bots, and identify zero-day intrusions in real time at the edge.

Responsibilities

• Build edge intelligence: design and train ML models for anomaly detection, bot mitigation, and zero-day intrusion detection.
• Deploy at scale with low latency: implement and optimize inference models to process massive volumes of HTTP traffic with ultra-low latency.
• Turn attacks into data: collaborate with offensive security engineers to understand attack vectors, simulate threats, and generate datasets for training.
• Automate the defense: create data pipelines to ingest traffic logs continuously, monitor model performance, detect concept drift, and automate retraining using current threat intelligence.

Requirements

• ML & programming: fluency in Python and SQL, with deep proficiency in PyTorch, TensorFlow, and/or Scikit-Learn.
• Production ML: experience deploying ML models to production for high-throughput, low-latency scenarios.
• Security & protocols: strong understanding of web security, HTTP, and common attacks (e.g., OWASP Top 10, L7 DDoS, credential stuffing).
• Edge & CDN/WAF: familiarity with edge computing platforms such as Cloudflare, Fastly, and AWS Edge, plus CDN/WAF concepts.
• Engineering fundamentals: ability to build production-ready services in Python, Rust, TypeScript, or similar to integrate models into the stack.
• Cloud & data: experience with GCP/AWS and managing large-scale datasets.
• LLMs/Agents: experience with LLMs and Agents.
• Collaboration: fully remote distributed team; ability to work autonomously and communicate complex technical concepts in English and Portuguese.

Nice to Have

• Direct experience with the Cloudflare ecosystem (Cloudflare Workers, Cloudflare WAF, Cloudflare Workers AI).
• Background in cybersecurity (IDS/IPS, threat hunting, malware analysis).
• Experience with payment security (e.g., PCI DSS, card tokenization, acquiring flows).
• Open-source security/ML contributions, security research publications, or CTF participation.